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| − | + | <h3 style="margin: 13pt 0cm"><span style="font-size: 14pt; line-height: 173%; font-family: Calibri"><font face="times new roman,times">SIST Spring Course: Natural Language Processing</font></span></h3><p><strong><span style="font-size: 14pt; font-family: Calibri"><font face="times new roman,times" size="3">Course Description</font></span></strong></p><p><strong><span style="font-size: 14pt; font-family: Calibri"></span></strong><font face="times new roman,times"><span style="font-size: 12pt; font-family: Calibri">Natural language processing is a research involving computer science, linguistics and cognitive science. This course gives systematic introduction of research topics on natural language processing, including morphology, lexicology, syntax, semantics, pragmatics and dialogue. To help the master students understand the natural language processing techniques easily, this course focuses on real applications and systems in course lectures. The following points make this course worth selecting. <span> </span></span><span style="font-size: 12pt; font-family: Calibri"> </span></font></p><p><font face="times new roman,times"><span style="font-size: 12pt; font-family: Calibri"></span><span style="font-size: 12pt; font-family: Calibri">1. This course makes use of the classroom as technologic understanding and discussion and the lab for practicing on real natural language processing algorithms and systems. Students are encouraged to come up with novel ideas in course projects. And three special interest groups will be setup to accomplish three interesting projects. <br></span><span style="font-size: 12pt; font-family: Calibri">2. Information presented in this course comes from most recent publications on natural language processing. This is helpful for students to grasp technological core as well as popular trends in this area. <br></span><span style="font-size: 12pt; font-family: Calibri">3. This course will be presented in English. On the one hand, master students from oversea are encouraged to attend without special training on Chinese language.<span> </span>On the other hand, this course is also helpful to Chinese native speakers to follow international development of this research area. In course project, they will also be trained on English communication. </span><span style="font-size: 12pt; font-family: Calibri"> </span></font></p><p><font face="times new roman,times"><span style="font-size: 12pt; font-family: Calibri"></span><span style="font-size: 12pt; font-family: Calibri">This course targets at master students from departments of <strong>computer science</strong>, <strong>automation</strong> and <strong>electronics</strong> who already have theoretical background of <strong>artificial intelligence</strong>. Besides, this course is also very helpful for master students from department of Chinese language whose major is <strong>computation linguistics</strong>. </span><span style="font-size: 12pt; font-family: Calibri"> </span></font></p><p><font face="times new roman,times"><span style="font-size: 12pt; font-family: Calibri"></span><strong><span style="font-size: 14pt; font-family: Calibri">Syllabus</span></strong> </font></p><table border="1" cellspacing="0" cellpadding="0" width="629" class="MsoTableGrid" style="margin: auto auto auto 5.4pt; width: 471.5pt; border-collapse: collapse; border: medium none"><<tr><td width="51" valign="top" style="padding-right: 5.4pt; padding-left: 5.4pt; background: #ccffff; padding-bottom: 0cm; width: 38.45pt; padding-top: 0cm; border: windowtext 1pt solid"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">Week</font></span></td><td width="577" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: windowtext 1pt solid; padding-left: 5.4pt; background: #ccffff; padding-bottom: 0cm; border-left: #ece9d8; width: 433.05pt; padding-top: 0cm; border-bottom: windowtext 1pt solid"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">Topic</font></span></td></tr><tr><td width="51" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: windowtext 1pt solid; width: 38.45pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">1</font></span></td><td width="577" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: #ece9d8; width: 433.05pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><font face="times new roman,times"><span style="font-size: 12pt; font-family: Calibri">Course Introduction NLP</span><span style="font-size: 12pt; font-family: Calibri">Recent Development of Natural Language Processing Research</span></font></td></tr><tr><td width="51" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: windowtext 1pt solid; width: 38.45pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">2</font></span></td><td width="577" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: #ece9d8; width: 433.05pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">Lexical Analysis on Chinese Text and ICTCLAS</font></span></td></tr><tr><td width="51" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: windowtext 1pt solid; width: 38.45pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">3</font></span></td><td width="577" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: #ece9d8; width: 433.05pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">Dependency parsing and HIT Dpaser</font></span></td></tr><tr><td width="51" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: windowtext 1pt solid; width: 38.45pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">4</font></span></td><td width="577" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: #ece9d8; width: 433.05pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">WordNet and HowNet, the semantic knowledge base and applications</font></span></td></tr><tr><td width="51" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: windowtext 1pt solid; width: 38.45pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">5</font></span></td><td width="577" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: #ece9d8; width: 433.05pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">Guest Lecture on Natural Language Dialogue System</font></span></td></tr><tr><td width="51" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: windowtext 1pt solid; width: 38.45pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">6</font></span></td><td width="577" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: #ece9d8; width: 433.05pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">Corpus and Corpus based NLP Techniques</font></span></td></tr><tr><td width="51" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: windowtext 1pt solid; width: 38.45pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">7</font></span></td><td width="577" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: #ece9d8; width: 433.05pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">Guest Lecture on Machine Translation</font></span></td></tr><tr><td width="51" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: windowtext 1pt solid; width: 38.45pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">8</font></span></td><td width="577" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: #ece9d8; width: 433.05pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">Information Extraction Technologies</font></span></td></tr><tr><td width="51" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: windowtext 1pt solid; width: 38.45pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">9</font></span></td><td width="577" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: #ece9d8; width: 433.05pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">Machine Learning Approaches to NLP</font></span></td></tr><tr><td width="51" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: windowtext 1pt solid; width: 38.45pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">10</font></span></td><td width="577" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: #ece9d8; width: 433.05pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">Modern Information Retrieval</font></span></td></tr><tr><td width="51" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: windowtext 1pt solid; width: 38.45pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">11</font></span></td><td width="577" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: #ece9d8; width: 433.05pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">Text classification and clustering</font></span></td></tr><tr><td width="51" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: windowtext 1pt solid; width: 38.45pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">12</font></span></td><td width="577" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: #ece9d8; width: 433.05pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">Guest Lecture on NLP evaluation</font></span></td></tr><tr><td width="51" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: windowtext 1pt solid; width: 38.45pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">13</font></span></td><td width="577" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; padding-bottom: 0cm; border-left: #ece9d8; width: 433.05pt; padding-top: 0cm; border-bottom: windowtext 1pt solid; background-color: transparent"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">Text Mining</font></span></td></tr><tr><td width="51" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; background: #f3f3f3; padding-bottom: 0cm; border-left: windowtext 1pt solid; width: 38.45pt; padding-top: 0cm; border-bottom: windowtext 1pt solid"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">14</font></span></td><td width="577" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; background: #f3f3f3; padding-bottom: 0cm; border-left: #ece9d8; width: 433.05pt; padding-top: 0cm; border-bottom: windowtext 1pt solid"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">Course project presentation (1)</font></span></td></tr><tr><td width="51" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; background: #f3f3f3; padding-bottom: 0cm; border-left: windowtext 1pt solid; width: 38.45pt; padding-top: 0cm; border-bottom: windowtext 1pt solid"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">15</font></span></td><td width="577" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; background: #f3f3f3; padding-bottom: 0cm; border-left: #ece9d8; width: 433.05pt; padding-top: 0cm; border-bottom: windowtext 1pt solid"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">Course project presentation (2)</font></span></td></tr><tr><td width="51" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; background: #f3f3f3; padding-bottom: 0cm; border-left: windowtext 1pt solid; width: 38.45pt; padding-top: 0cm; border-bottom: windowtext 1pt solid"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">16</font></span></td><td width="577" valign="top" style="border-right: windowtext 1pt solid; padding-right: 5.4pt; border-top: #ece9d8; padding-left: 5.4pt; background: #f3f3f3; padding-bottom: 0cm; border-left: #ece9d8; width: 433.05pt; padding-top: 0cm; border-bottom: windowtext 1pt solid"><span style="font-size: 12pt; font-family: Calibri"><font face="times new roman,times">Course project presentation (3)</font></span></td></tr></table><p><font face="times new roman,times"><strong><span style="font-size: 14pt; font-family: Calibri"> </span></strong><strong><span style="font-size: 14pt; font-family: Calibri">Reference textbooks</span></strong></font></p><p><font face="times new roman,times"><strong><span style="font-size: 14pt; font-family: Calibri"></span></strong><span style="font-size: 12pt; font-family: Calibri">1. <em>Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition</em> by Daniel Jurafsky and James H. Martin, Prentice Hall Press<br></span></font><font face="times new roman,times"><span style="font-size: 12pt; font-family: Calibri">2. <em>Foundation of Statistical Natural Language Processing</em> by Christopher D. Manning and Hinrich Schütze</span><span style="font-size: 12pt; font-family: 宋体">，</span><span style="font-size: 12pt; font-family: Calibri">MIT Press.<br></span><span style="font-size: 12pt; font-family: Calibri">3. <em>Foundations of Computational Linguistics: Human-Computer Communication in Natural Language</em> by Roland Hausser, Springer.<br></span><span style="font-size: 12pt; font-family: Calibri">4. <em>Handbook for Natural Language Processing</em>, edited by Robert Dale, Springer<font color="#000000">.</font></span><strong><span style="font-size: 14pt; font-family: Calibri"><font color="#000000"> </font></span></strong></font></p> | |
2014年8月22日 (五) 01:28的版本
SIST Spring Course: Natural Language Processing
Course Description
Natural language processing is a research involving computer science, linguistics and cognitive science. This course gives systematic introduction of research topics on natural language processing, including morphology, lexicology, syntax, semantics, pragmatics and dialogue. To help the master students understand the natural language processing techniques easily, this course focuses on real applications and systems in course lectures. The following points make this course worth selecting.
1. This course makes use of the classroom as technologic understanding and discussion and the lab for practicing on real natural language processing algorithms and systems. Students are encouraged to come up with novel ideas in course projects. And three special interest groups will be setup to accomplish three interesting projects.
2. Information presented in this course comes from most recent publications on natural language processing. This is helpful for students to grasp technological core as well as popular trends in this area.
3. This course will be presented in English. On the one hand, master students from oversea are encouraged to attend without special training on Chinese language. On the other hand, this course is also helpful to Chinese native speakers to follow international development of this research area. In course project, they will also be trained on English communication.
This course targets at master students from departments of computer science, automation and electronics who already have theoretical background of artificial intelligence. Besides, this course is also very helpful for master students from department of Chinese language whose major is computation linguistics.
Syllabus
| Week | Topic |
| 1 | Course Introduction NLPRecent Development of Natural Language Processing Research |
| 2 | Lexical Analysis on Chinese Text and ICTCLAS |
| 3 | Dependency parsing and HIT Dpaser |
| 4 | WordNet and HowNet, the semantic knowledge base and applications |
| 5 | Guest Lecture on Natural Language Dialogue System |
| 6 | Corpus and Corpus based NLP Techniques |
| 7 | Guest Lecture on Machine Translation |
| 8 | Information Extraction Technologies |
| 9 | Machine Learning Approaches to NLP |
| 10 | Modern Information Retrieval |
| 11 | Text classification and clustering |
| 12 | Guest Lecture on NLP evaluation |
| 13 | Text Mining |
| 14 | Course project presentation (1) |
| 15 | Course project presentation (2) |
| 16 | Course project presentation (3) |
Reference textbooks
1. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition by Daniel Jurafsky and James H. Martin, Prentice Hall Press
2. Foundation of Statistical Natural Language Processing by Christopher D. Manning and Hinrich Schütze，MIT Press.
3. Foundations of Computational Linguistics: Human-Computer Communication in Natural Language by Roland Hausser, Springer.
4. Handbook for Natural Language Processing, edited by Robert Dale, Springer.
